【摘要】：在現(xiàn)代工業(yè)生產(chǎn)中,隨著科學技術(shù)的進步與發(fā)展,作為主要生產(chǎn)工具的機械設(shè)備一方面不斷向復雜、高速、高效、大型自動化方面發(fā)展,另一方面卻又面臨更加苛刻的工作和運行環(huán)境。滾動軸承是石化、電力、冶金、機械、航空航天以及軍事工業(yè)部門中使用最廣泛的機械零件,也是最易損傷的部件之一,其工作狀態(tài)是否正常對于整個機械設(shè)備乃至整條生產(chǎn)線的運行狀態(tài)有著重大的影響。因此,如何有效地診斷和評估設(shè)備的運行狀態(tài),從而能夠及時采取措施以防止突發(fā)事故的發(fā)生是當前迫切需要解決的問題。 一般來說,機械設(shè)備在運行過程中,都會經(jīng)歷從正常直至完全失效的過程,在這過程中,機械設(shè)備總會經(jīng)歷一系列不同程度的性能退化狀態(tài)。因此,在機械設(shè)備性能退化的過程中,如果能夠準確地監(jiān)測到其性能退化程度,那么就可以有針對性地制定合理的機械設(shè)備維護計劃,從而既可以防止設(shè)備因故障而臨時失效,又可以合理安排生產(chǎn)。當設(shè)備發(fā)生異常時,性能退化評估可以及時發(fā)現(xiàn)并進行故障診斷,防止故障進一步加深,從而提高設(shè)備的利用率,縮短設(shè)備的停機維修時間。為此,本文以滾動軸承為研究對象,深入開展了基于特征加權(quán)連續(xù)隱馬爾可夫模型(CHMM:Continuous Hidden Markov Model)的故障診斷與性能退化評估的理論體系和技術(shù)方法的研究,具體內(nèi)容如下: 1)從理論分析與工程應(yīng)用的角度出發(fā),簡要闡述了論文的選題背景和研究意義。針對機械設(shè)備故障診斷,論述了信號分析與處理技術(shù)、智能故障診斷方法、性能退化評估方法等方面的國內(nèi)外研究現(xiàn)狀與發(fā)展趨勢。在此基礎(chǔ)上,給出了本文的主要研究內(nèi)容。 2)詳細介紹了HMM的基本理論,并著重論述了連續(xù)HMM的理論,針對其算法中存在的數(shù)據(jù)下溢、參數(shù)初始化等問題給出相應(yīng)的解決方法。最后,本文簡單介紹基于連續(xù)HMM故障診斷的基本思想與流程。 3)研究了基于二階循環(huán)平穩(wěn)分析的譜相關(guān)密度組合切片特征提取方法,并將其與連續(xù)HMM相結(jié)合,提出了一種適用于滾動軸承故障診斷的方法。通過對其進行實驗分析,驗證了該方法的有效性與可行性。通過與多種特征提取方法比較,結(jié)果表明,該方法具有分類準確率高,分類離散度大等優(yōu)點,可適用于滾動軸承的故障診斷。 4)研究了基于特征加權(quán)的連續(xù)HMM故障診斷方法。在描述設(shè)備狀態(tài)時通常需要提取多種不同特征,從而形成高維特征。本文研究了基于距離評估技術(shù)的特征降維方法,并對其進行參數(shù)補償,對高維特征進行分析,可以得到對分類特征明顯的敏感特征,有效的解決了因為人為隨機選取特征進行分類造成的分類結(jié)果可靠度不高及高維特征災(zāi)難等問題。最后通過兩個軸承實驗進行了驗證,與傳統(tǒng)方法相比,該方法在保證分類準確率的前提下,有效降低了模式分類器的計算復雜度,提高模式類別的可分性,增強了分類結(jié)果的可靠性。 5)研究了基于不完備數(shù)據(jù)與完備數(shù)據(jù)兩種情況下的滾動軸承性能退化評估方法,特別是在完備數(shù)據(jù)情況下,提出了一種基于特征加權(quán)的連續(xù)HMM性能退化評估方法。利用滾動軸承的加速疲勞試驗得到的全壽命周期數(shù)據(jù),對該評估方法進行了驗證,試驗分析結(jié)果表明基于特征加權(quán)的連續(xù)HMM性能退化評估方法具有識別性能好,計算量小等優(yōu)點。在完備數(shù)據(jù)情況下,考察了性能退化評估模型的推廣性,并用試驗數(shù)據(jù)進行了交叉驗證,結(jié)果表明同一種退化模式下的模型具有較好的推廣性。
[Abstract]:In the modern industrial production, with the progress and development of science and technology, the mechanical equipment, which is the main production tool, is continuously developing in the aspects of complex, high-speed, high-efficiency and large-scale automation, and on the other hand, it faces more demanding work and operation environment. The rolling bearing is one of the most widely used mechanical parts in the petrochemical, electric, metallurgical, mechanical, aerospace and military industrial sectors. It is also one of the most damaged parts. The working state of the rolling bearing is of great influence on the operation state of the whole mechanical equipment and the whole production line. Therefore, how to effectively diagnose and evaluate the operation state of the equipment, so as to be able to take measures in time to prevent the occurrence of a sudden accident is a problem that is urgently needed to be solved. In general, in the course of operation, the mechanical equipment will experience a process from normal to complete failure, during which the mechanical equipment will experience a series of varying degrees of performance degradation Therefore, in the process of the degradation of the performance of the mechanical equipment, if the performance degradation degree of the equipment can be accurately monitored, a reasonable maintenance plan of the mechanical equipment can be established in a targeted manner, so that the equipment can be prevented from being temporarily disabled due to failure, and the equipment can be arranged reasonably When the equipment is abnormal, the performance degradation evaluation can timely find and fault the fault, and prevent the fault from further deepening, so as to improve the utilization rate of the equipment and shorten the equipment shutdown and maintenance. In this paper, the theoretical system and technical method of the evaluation of fault diagnosis and performance degradation based on the feature-weighted continuous hidden Markov Model (CHMM: Continuous Hidden Markov Model) are carried out in this paper. (1) From the point of view of the theory analysis and the engineering application, the paper gives a brief account of the background and the research of the thesis. In view of the fault diagnosis of mechanical equipment, the present situation and development of the research and abroad of signal analysis and processing technology, intelligent fault diagnosis method and performance degradation assessment method are discussed. In this paper, the main research of this paper is given. In this paper, the basic theory of HMM is introduced in detail, and the theory of continuous HMM is discussed in detail. The problems of data underflow, parameter initialization and so on in the algorithm are given. Finally, this paper briefly introduces the basic principle of fault diagnosis based on continuous HMM In this paper, the feature extraction method of spectral correlation density combined slice based on the second-order cyclic stability analysis is studied and combined with the continuous HMM, a new method is presented for rolling bearing. The method of fault diagnosis is verified by the experimental analysis. The results show that the method has the advantages of high classification accuracy, large classification, and the like, and can be applied to rolling. The fault diagnosis of the bearing (4) is based on the feature-weighted continuous fault diagnosis. in describe that state of a device, it is often necessary to extract a number of different features In this paper, the feature reduction method based on distance assessment technology is developed, and the parameter compensation is carried out to analyze the high-dimensional features and the classification can be obtained. is characterized by obvious sensitive characteristics and effectively solves the problem that the reliability of the classification result caused by the classification of the human-made random selection characteristic is not high, At last, it is verified by two bearing experiments. Compared with the traditional method, the method effectively reduces the computational complexity of the mode classifier, improves the separability and the enhancement of the mode class, The reliability of the classification results is given.5) The method of evaluating the performance of rolling bearing based on incomplete data and complete data is studied, especially in the case of complete data, a continuous evaluation method based on feature weight is proposed. The evaluation method of the performance degradation of the HMM is carried out by using the full-life cycle data obtained by the accelerated fatigue test of the rolling bearing, and the test result shows that the method for evaluating the performance degradation of the continuous HMM based on the characteristic weighting has the advantages of identification, In the condition of complete data, the generalization of the performance degradation evaluation model is investigated, and the cross-verification is carried out with the test data. The results show that in the same degradation mode
【學位授予單位】：上海交通大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH165.3

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本文編號：2440596